markdown stringlengths 44 160k | filename stringlengths 3 39 |
|---|---|
# Google BigQuery {: #google-bigquery }
## Supported authentication {: #supported-authentication }
- OAuth
- Service account ([public preview](bigq-service-acct))
## Prerequisites {: #prerequisites }
The following is required before connecting to Google BigQuery in DataRobot:
- A Google account [authenticated with... | dc-bigquery |
# Amazon Athena {: #amazon-athena }
## Supported authentication {: #supported-authentication }
- AWS Credential
## Prerequisites {: #prerequisites }
The following is required before connecting to Amazon Athena in DataRobot:
- [AWS account](https://docs.aws.amazon.com/athena/latest/ug/setting-up.html){ target=_blan... | dc-athena |
# MySQL {: #mysql }
## Supported authentication {: #supported-authentication }
- Username/password
## Prerequisites {: #prerequisites }
The following is required before connecting to MySQL in DataRobot:
- MySQL account
## Required parameters {: #required-parameters }
The table below lists the minimum required fi... | dc-mysql |
# Exasol {: #exasol }
## Supported authentication {: #supported-authentication }
- Username/password
## Prerequisites {: #prerequisites }
The following is required before connecting to Exasol in DataRobot:
- Exasol account
## Required parameters {: #required-parameters }
The table below lists the minimum require... | dc-exasol |
# Oracle {: #oracle }
There are two data connection types for Oracle Database: Service Name and SID. Use the appropriate parameters for the connection path you use to connect to Oracle Database.
## Supported authentication {: #supported-authentication }
- Username/password for both Service Name and SID
## Prerequis... | dc-oracle |
# kdb+ {: #kdb }
## Supported authentication {: #supported-authentication }
- Username/password
## Prerequisites {: #prerequisites }
The following is required before connecting to kdb+ in DataRobot:
- kdb+ account
## Required parameters {: #required-parameters }
The table below lists the minimum required fields ... | dc-kdb |
# Supported databases {: #supported-databases }
<!--- When bumping versions, also update `datarobot_docs/en/api/reference/batch-prediction-api/index.md` --->
DataRobot with JDBC 4.1 has tested support for the following databases.
| Database | Version | Driver Jar |
|-----|-----|-----|
| [Amazon Redshift](dc-redshif... | index |
# SAP HANA {: #sap-hana }
## Supported authentication {: #supported-authentication }
- Username/password
## Prerequisites {: #prerequisites }
The following is required before connecting to SAP HANA in DataRobot:
- SAP HANA account
## Required parameters {: #required-parameters }
The table below lists the minimum... | dc-sap-hana |
# Amazon S3 {: #amazon-s3 }
## Supported authentication {: #supported-authentication }
- AWS Credential
## Prerequisites {: #prerequisites }
The following is required before connecting to Amazon S3 in DataRobot:
- Amazon S3 account
## Required parameters {: #required-parameters }
The table below lists the minimu... | dc-s3 |
# Presto {: #presto }
## Supported authentication {: #supported-authentication }
- Username/password
## Prerequisites {: #prerequisites }
The following is required before connecting to Presto in DataRobot:
- Presto account
## Required parameters {: #required-parameters }
The table below lists the minimum require... | dc-presto |
# Amazon Redshift {: #amazon-redshift }
## Supported authentication {: #supported-authentication }
- Username/password
## Prerequisites {: #prerequisites }
The following is required before connecting to Redshift in DataRobot:
- Amazon Redshift account
## Required parameters {: #required-parameters }
The table be... | dc-redshift |
---
title: Predictions
description: You can make predictions with models using engineered features the same way as with any other DataRobot model, using the Make Predictions or the Deploy tab.
---
# Predictions {: #predictions }
You can make predictions with models using engineered features in the same way as you do... | fd-predict |
---
title: Feature Discovery projects
description: How to create a project from multiple datasets. You define the relationships. Feature Discovery aggregates the secondary datasets to enrich the primary dataset.
---
# Feature Discovery projects {: #feature-discovery-projects }
Feature Discovery is based on relations... | fd-overview |
---
title: Feature Discovery
description: With DataRobot, you can automatically discover and generate new features from multiple datasets, without consolidating manually.
---
# Feature Discovery {: #feature-discovery }
To deploy AI across the enterprise, you must be able to access relevant features to make the best ... | index |
---
title: Derived features
description: Complete details on new features DataRobot derives during Feature Discovery, and how to work with these features on the Data page after EDA2 completes.
---
# Derived features {: #derived-features }
The Feature Discovery process uses a variety of heuristics to determine the li... | fd-gen |
---
title: Time-aware feature engineering
description: How to configure time-aware feature engineering using only information available before the prediction point.
---
# Time-aware feature engineering {: #time-aware-feature-engineering }
Time-based feature engineering in Feature Discovery projects involves use of a... | fd-time |
---
title: Leverage AI accelerators
description: Understand how AI accelerators work and how you can leverage them to get value from code-first machine learning workflows.
---
# Leverage AI accelerators {: #leverage-ai-accelerators }
After reviewing [how to get started with DataRobt as a code-first user](gs-code) an... | gs-ai |
---
title: Code-first experience
description: Get started with DataRobot's code-first experience. Build and execute notebooks and leverage AI accelerators.
---
# Code-first experience {: #code-first-experience }
Get started with DataRobot's code-first experience. Build and execute notebooks and leverage AI accelerato... | index |
---
title: Work with notebooks
description: Provides an overview of how to engage with DataRobot's code-centric platform.
---
# Work with notebooks {: #work-with-notebooks }
Follow five simple steps to get started with DataRobot's code-first experience. This page will outline how to get value out of DataRobot Notebo... | gs-code |
---
title: Models in production
description: Get started with DataRobot MLOps by deploying a DataRobot model to DataRobot infrastructure.
---
# Models in production {: #models-in-production }
DataRobot MLOps provides a central hub to [deploy](deployment/index), [monitor](monitor/index), [manage](manage-mlops/index),... | gs-mlops |
---
title: Work with data (Classic)
description: An overview of the tools DataRobot Classic provides for importing, preparing, and managing data for machine learning.
---
# Work with data (Classic) {: #work-with-data-classic }
DataRobot knows that high-quality data is integral to the ML workflow—from importing and cl... | gs-data |
---
title: DataRobot Classic
description: Get started with DataRobot's value-driven AI. Analyze data, create and deploy models, and leverage code-first accelerators and notebooks.
---
# DataRobot Classic {: #datarobot-classic }
Get started with DataRobot's classic experience. Analyze data, create and deploy models, ... | index |
---
title: Fundamentals of DataRobot Classic
description: Learn about modeling methods supported in DataRobot Classic, as well as the modeling lifecycle.
---
# Fundamentals of DataRobot Classic {: #fundamentals-of-datarobot-classic }
DataRobot uses automated machine learning (AutoML) to build models that solve real-... | gs-dr-fundamentals |
---
title: Start modeling
description: Provides a quick overview of modeling and deploying models with DataRobot.
---
# Start modeling {: #start-modeling }
To build models in DataRobot, you first create a project by importing a dataset, selecting a target feature, and clicking **Start** to begin the modeling process... | gs-model |
---
title: Workbench capabilities
description: An evolving comparison of capabilities available in DataRobot Classic and Workbench.
---
# Workbench capabilities {: #workbench-capabilities }
{% include 'includes/wb-capability-matrix.md' %}
| gs-wb-capabilities |
---
title: Workbench experimentation
description: Get started with DataRobot's value-driven AI. Analyze data, create models, and leverage code-first accelerators and notebooks.
---
# Workbench experimentation {: #workbench-experimentation }
Get started with DataRobot's Workbench experience. Analyze data, create mode... | index |
---
title: Fundamentals of Workbench
description: Understand the components of the DataRobot Workbench interface, including the architecture, some sample workflows, and directory landing page.
---
# Fundamentals of Workbench {: #fundamentals-of-workbench }
{% include 'includes/wb-overview.md' %}
| gs-wb-fundamentals |
---
title: Work with with data (Workbench)
description: An overview of the tools DataRobot provides in Workbench for importing, preparing, and managing data for machine learning.
---
# Work with with data (Workbench) {: #work-with-data-workbench }
DataRobot knows that high-quality data is integral to the ML workflow... | gs-wb-data |
---
title: Build experiments
description: Build models in minutes, gain insights, compare results, then move your models into production.
---
# Build experiments {: #build-experiments }
DataRobot takes the data you provide, generates multiple machine learning models, and recommends the best model to put into product... | gs-wb-experiments |
---
title: Get help
description: This help section provides basic account access troubleshooting and quick, task-based instructions for success in modeling.
---
# Get help {: #get-help }
## Troubleshooting {: #troubleshooting }
This section provides information on troubleshooting DataRobot authentication and access... | index |
---
title: DataRobot in 5
description: A short overview of the steps involved in building and deploying models in DataRobot.
---
# DataRobot in 5 {: #datarobot-in-5 }
Building and deploying models in DataRobot—regardless of the data handling, modeling options, prediction methods, and deployment actions—c... | index |
---
title: DataRobot status
description: Status page announcements provide information on service outages, scheduled maintenance, and historical uptime.
---
# Check platform status {: #check-platform-status }
DataRobot performs service maintenance regularly. Although most maintenance will occur unnoticed, some may c... | status-help |
---
title: Need help signing in?
description: This article addresses common questions related to signing up or signing in to the DataRobot AI Platform or the DataRobot Community.
---
# Need help signing in?
This article addresses common questions related to signing up or signing in to the DataRobot AI Platform or th... | signin-help |
---
title: Troubleshooting the Worker Queue
description: If you expect to be able to increase your worker count but cannot, check the reasons described here.
---
# Troubleshooting the Worker Queue {: #troubleshooting-the-worker-queue }
{% include 'includes/worker-queue-tbsht-include.md' %}
| workers-help |
---
title: Troubleshooting 2FA
description: Help with two-factor authentication (2FA), an opt-in feature that provides additional security for DataRobot users.
---
# Troubleshooting 2FA {: #troubleshooting-2fa }
Two-factor authentication (2FA) is an opt-in feature that provides additional security for DataRobot use... | 2fa-help |
---
title: Troubleshooting
description: View common issues and troubleshooting tips for a smooth DataRobot experience.
---
# Troubleshooting {: #troubleshooting }
This section provides information on troubleshooting DataRobot authentication and access:
Topic | Describes...
----- | ------------
[Trial FAQ](trial-faq)... | index |
---
title: Troubleshooting the Python client
description: Review cases that can cause issues with using the Python client and known fixes.
---
# Troubleshooting the Python client {: #troubleshooting-the-python-client }
This page outlines cases that can cause issues with using the Python client and provides known fix... | py-help |
---
title: Trial FAQ
description: Questions and answers about DataRobot's self-service trial experience.
---
# Trial FAQ {: #trial-faq }
??? faq "What is self-service SaaS?"
DataRobot's _self-service_ SaaS includes the same capabilities and features that are available in the managed AI Platform enterprise software... | trial-faq |
---
title: Tutorials
description: Tutorials provide quick, task-based instructions for success in modeling.
---
# Tutorials {: #tutorials }
DataRobot offers a variety of tutorials to assist you in using different aspects of the application, outlined below:
Topic | Describes how to...
----- | ------
[Prepare learnin... | index |
---
dataset_name: 1k_diabetes-train.csv
expiration_date: 10-10-2024
owner: misha.yakubovskiy@datarobot.com
domain: core-modeling
title: Select a target
description: This tutorial provides instructions to select a prediction target for your project.
url: https://docs.datarobot.com/en/tutorials/creating-ai-models/tut-tar... | tut-target |
---
title: Set the modeling mode
dataset_name: 1k_diabetes-train.csv
description: This tutorial provides instructions to select a modeling mode for your project.
domain: core-modeling
expiration_date: 10-10-2024
owner: izzy@datarobot.com
url: https://docs.datarobot.com/en/tutorials/creating-ai-models/tut-model-mode.htm... | tut-model-mode |
---
title: Create AI models
description: The tutorials in this section provide quick, task-based instructions for achieving common tasks related to modeling.
---
# Create AI models {: #create-ai-models }
The content in this section provides quick FAQ answers as well as task-based tutorials for achieving common tasks... | index |
---
title: Analyze feature associations
dataset_name: N/A
description: How to use a Feature Association matrix to visualize relationships among your features.
domain: platform
expiration_date: 10-10-2024
owner: izzy@datarobot.com
url: docs.datarobot.com/docs/tutorials/prep-learning-data/analyze-feature-associations.htm... | analyze-feature-associations |
---
title: Assess data quality during EDA
dataset_name: N/A
description: How DataRobot performs Exploratory Data Analysis (EDA) and how to assess the quality of your data at each stage of EDA.
domain: platform
expiration_date: 10-10-2024
owner: izzy@datarobot.com
title: Assess data quality during EDA
url: docs.datarobo... | assess-data-quality-eda |
---
title: Analyze features using histograms
dataset_name: N/A
description: How to analyze numeric features using histograms, which let you analyze the distribution of values and view outlier values.
domain: platform
expiration_date: 10-10-2024
owner: izzy@datarobot.com
url: docs.datarobot.com/docs/tutorials/prep-learn... | analyze-features-using-histograms |
---
title: Import data to DataRobot
dataset_name: N/A
description: How to import data to DataRobot by uploading a local file, specifying a URL, or connecting to a data source.
domain: platform
expiration_date: 10-10-2024
owner: izzy@datarobot.com
url: docs.datarobot.com/docs/more-info/tutorials/prep-learning-data/impor... | import-data-dr-tutorial |
---
title: Manage data with the AI Catalog
dataset_name: N/A
description: How to import data to the AI Catalog and how to use the catalog to prepare, blend, and create a project from your data.
domain: platform
expiration_date: 10-10-2024
owner: izzy@datarobot.com
url: docs.datarobot.com/docs/tutorials/prep-learning-da... | ai-catalog-tutorial |
---
title: Work with feature lists
dataset_name: N/A
description: How to use automatically generated feature lists and build your own as training data for machine learning.
domain: platform
expiration_date: 10-10-2024
owner: izzy@datarobot.com
url: docs.datarobot.com/docs/tutorials/prep-learning-data/work-with-feature-... | work-with-feature-lists |
---
title: Enrich data using Feature Discovery
dataset_name: N/A
description: How Feature Discovery helps you combine datasets of different granularities and perform automated feature engineering.
domain: platform
expiration_date: 10-10-2024
owner: izzy@datarobot.com
title: Enrich data using Feature Discovery
url: docs... | enrich-data-using-feature-discovery |
---
title: Prepare learning data
description: The tutorials in this section provide quick, task-based instructions that will help you with common data preparation tasks.
---
# Prepare learning data {: #prepare-learning-data }
The content in this section provides quick FAQ answers as well as task-based tutorials for ... | index |
---
title: Analyze frequent values
dataset_name: N/A
description: How to use the Frequent Values chart, a histogram that shows the number of rows containing each value of a feature.
domain: platform
expiration_date: 10-10-2024
owner: izzy@datarobot.com
url: docs.datarobot.com/docs/tutorials/prep-learning-data/analyze-f... | analyze-frequent-values |
---
dataset_name: 10k_diabetes.xlsx
expiration_date: 10-10-2024
owner: izzy@datarobot.com
domain: trust-explainable-ai
title: Understand the Word Cloud
description: This tutorial provides instructions to access and understand the Word Cloud insight.
url: https://docs.datarobot.com/en/tutorials/explore-ai-insights/tut-w... | tut-wordcloud |
---
title: Interpret the Leaderboard
dataset_name: 1k_diabetes-train.csv
expiration_date: 10-10-2024
owner: izzy@datarobot.com
domain: core-modeling
description: This tutorial provides an overview of how to read the Leaderboard tab and available actions.
url: https://docs.datarobot.com/en/tutorials/explore-ai-insights/... | tut-read-leaderboard |
---
dataset_name: predictive_maintenance_train.csv
expiration_date: 10-10-2024
owner: tony.martin@datarobot.com
domain: time-series
title: Use anomaly detection with time series
description: This tutorial describes working with anomaly detection models in DataRobot.
url: https://docs.datarobot.com/en/tutorials/explore-... | tut-ts-anomaly-detection |
---
title: Explore AI insights
description: The tutorials and FAQ in this section provide quick, task-based instructions for achieving common tasks related to modeling.
---
# Explore AI insights {: #explore-ai-insights }
The tutorials in this section provide quick, task-based instructions for achieving common tasks ... | index |
---
title: Portable prediction methods
description: Learn about DataRobot's available methods for portable predictions.
---
# Portable prediction methods {: #batch-scoring-methods }
{% include 'includes/port-pred-options.md' %}
| index |
---
title: Qlik predictions
description: Submit Qlik data for scoring via the prediction API and a sample code snippet.
---
# Qlik predictions {: #qlik-predictions }
To integrate with Qlik, DataRobot provides a code snippet containing the commands and identifiers necessary to submit Qlik data for scoring using the [... | integration-code-snippets |
---
title: Prediction API snippets
description: How to adapt downloadable DataRobot Python code to submit a CSV or JSON file for scoring and integrate it into a production application via the Prediction API.
---
# Prediction API snippets {: #prediction-api-snippets }
DataRobot provides sample Python code containing ... | code-py |
---
title: Real-time scoring methods
description: Learn about DataRobot's available methods for making real-time predictions.
---
# Real-time scoring methods {: #real-time-scoring-methods }
Make real-time predictions by sending an HTTP request for a model via a synchronous call. After DataRobot receives the request, ... | index |
---
title: Batch prediction methods
description: Learn about DataRobot's available methods for scoring large files efficiently.
---
# Batch prediction methods {: #batch-prediction-methods }
DataRobot offers a variety of methods to efficiently score large files via batch predictions:
Method | Description
------ | ---... | index |
---
title: Manage batch jobs
description: View and manage running or complete jobs.
---
# Manage batch jobs {: #manage-batch-jobs }
To access batch jobs, navigate to **Deployments > Batch Jobs**. You can view and manage all running or complete jobs. Any prediction or monitoring jobs created for deployments appear on ... | batch-jobs |
---
title: Batch prediction scripts
description: Use the Prediction API with these scripts to score large files efficiently.
---
# Batch prediction scripts {: #batch-prediction-scripts }
The Batch prediction scripts are command-line tools for Windows, macOS, and Linux.
They wrap the [Batch Prediction API](batch-predi... | cli-scripts |
---
title: JAR structure
description: Review the structure of the downloadable Scoring Code JAR package.
---
# JAR structure {: #jar-structure }
Once you have downloaded the Scoring Code JAR package to your machine, you'll see that it has a well-organized structure:
## Root directory... | jar-package |
---
title: Scoring Code for time series projects
description: How to use the Scoring Code feature for qualifying time series models, allowing you to use DataRobot-generated models outside of the DataRobot platform.
---
# Scoring Code for time series projects {: #scoring-code-for-time-series-projects }
[Scoring Code]... | sc-time-series |
---
title: Scoring Code JAR integrations
description: How to import DataRobot Scoring Code JARs into external platforms.
---
# Scoring Code JAR integrations {: #scoring-code-jar-integrations }
!!! info "Availability information"
Contact your DataRobot representative for information on enabling the Scoring Code ... | sc-jar-integrations |
---
title: Backward-compatible Java API
description: Review the process of using scoring code with models created on different versions of DataRobot.
---
# Backward-compatible Java API {: #backward-compatible-java-api }
This section describes the process of using scoring code with models created on different version... | java-back-compat |
---
title: Download Scoring Code from a deployment
description: Download a Scoring Code JAR file directly from a DataRobot deployment.
---
# Download Scoring Code from a deployment {: #download-scoring-code-from-a-deployment }
!!! info "Availability information"
The behavior of deployments from which you downlo... | sc-download-deployment |
---
title: Download Scoring Code from the Leaderboard
description: Download a Scoring Code JAR file directly from the Leaderboard.
---
# Download Scoring Code from the Leaderboard {: #download-scoring-code-from-the-leaderboard }
You can download [Scoring Code](sc-overview) for models as pre-compiled JAR files (with ... | sc-download-leaderboard |
---
title: Scoring Code usage examples
description: Learn how to use DataRobot's Scoring Code feature.
---
# Scoring Code usage examples {: #scoring-code-usage-examples }
!!! info "Availability information"
Contact your DataRobot representative for information on enabling the Scoring Code feature.
Models displa... | quickstart-api |
---
title: Download Scoring Code from the Leaderboard (Legacy)
description: Download a Scoring Code JAR file directly from the Leaderboard as a legacy user.
---
# Download Scoring Code for legacy users {: #download-scoring-code-for-legacy-users }
Models displaying the SCORING CODE [indicator](leaderboard-ref#tags-an... | sc-download-legacy |
---
title: Scoring Code
description: How to export Scoring Code so that you can use DataRobot-generated models outside of the DataRobot platform.
---
# Scoring Code {: #scoring-code }
!!! info "Availability information"
Contact your DataRobot representative for information on enabling the Scoring Code feature.
... | index |
---
title: Generate Java models in an existing project
description: Retrain legacy models for which you want to download Scoring Code.
---
# Generate Java models in an existing project {: #generate-java-models-in-an-existing-project }
If you have projects that were created before the Scoring Code feature was enabled... | build-verify |
---
title: Scoring Code overview
description: How to use the Scoring Code feature for qualifying Leaderboard models, allowing you to use DataRobot-generated models outside of the DataRobot platform.
---
# Scoring Code overview {: #scoring-code-overview }
!!! info "Availability information"
Contact your DataRobo... | sc-overview |
---
title: Scoring at the command line
description: The following sections provide syntax for scoring at the command line.
keywords: Python, Java, source code, codegen, binary, source, scoring, transparent model, code validation, jar
---
# Scoring at the command line {: #scoring-at-the-command-line }
The following ... | scoring-cli |
---
title: Custom model Portable Prediction Server
description: How to download, build, and run the custom model Portable Prediction Server (PPS) to deploy a custom model to an external prediction environment.
---
# Custom model Portable Prediction Server {: #custom-model-portable-prediction-server }
The custom mode... | custom-pps |
---
title: Portable Prediction Server running modes
description: Learn how to configure the Portable Prediction Server for single-model or multi-model running mode.
---
# Portable Prediction Server running modes {: #portable-prediction-server-running-modes }
There are two model modes supported by the server: single-... | pps-run-modes |
---
title: Portable Prediction Server
description: Learn how to configure and execute DataRobot's Portable Prediction Server.
---
# Portable Prediction Server {: #portable-prediction-server }
The Portable Prediction Server (PPS) is a remote DataRobot execution environment for DataRobot model packages (`MLPKG` files)... | index |
---
title: Portable Prediction Server
description: How to use the Portable Prediction Server (PPS), which executes a DataRobot model package distributed as a self-contained Docker image.
---
# Portable Prediction Server {: #portable-prediction-server }
The Portable Prediction Server (PPS) is a DataRobot execution e... | portable-pps |
---
title: Portable batch predictions
description: How to use the portable batch predictions (PBP) with PPS and score data in a batch in an isolated environment.
---
# Portable batch predictions {: #portable-batch-predictions }
Portable batch predictions (PBP) let you score large amounts of data on disconnected env... | portable-batch-predictions |
---
title: DataRobot Prime
description: Learn how DataRobot Prime optimizes models for use outside the DataRobot application. You can build a DataRobot Prime model for most models on the Leaderboard.
---
# DataRobot Prime {: #datarobot-prime }
!!! info "Availability information"
The ability to create new DataRob... | index |
---
title: RuleFit export examples
description: Learn how to generate source code for a model as a Python module or Java class, and use DataRobot Prime with Python or Java.
---
# RuleFit export examples {: #ruleFit-export-examples }
You can generate source code for the model as a [Python module](#using-rulefit-with-p... | rulefit-examples |
---
title: Make a one-time batch prediction
description: Make a batch prediction for a deployed model with a dataset of any size. Learn about additional prediction options for time series deployments.
---
# Make a one-time batch prediction {: #make-a-one-time-batch-prediction }
Use the **Deployments > Make Predictio... | batch-pred |
---
title: Manage prediction job definitions
description:
---
# Manage prediction job definitions
To view and manage monitoring job definitions, select a deployment on the **Deployments** tab and navigate to the **Job Definitions > Prediction Jobs** tab.
Click the action me... | manage-pred-job-def |
---
title: Batch prediction UI
description: Use a deployment's batch prediction interface to score large files efficiently.
---
# Batch prediction UI {: #batch-scoring-methods }
To make batch predictions from the UI, you must first deploy a model. After deploying, navigate to the [**Make Predictions** tab](batch-pred... | index |
---
title: Schedule recurring batch prediction jobs
description: How to configure, execute, and schedule batch prediction jobs for deployed models.
---
# Schedule recurring batch prediction jobs {: #schedule-recurring-batch-prediction-jobs }
You might want to make a [one-time batch prediction](batch-pred), but you m... | batch-pred-jobs |
---
title: Snowflake prediction job examples
description: Configure prediction jobs with Snowflake connections.
---
# Snowflake prediction job examples {: #snowflake-prediction-job-examples }
There are two ways to set up a batch prediction job definition for Snowflake:
* Using a [JDBC connector with Snowflake](#jdb... | pred-job-examples-snowflake |
---
title: Prediction monitoring jobs
description: To integrate more closely with external data sources, monitoring job definitions allow DataRobot to monitor deployments running and storing feature data and predictions outside of DataRobot.
---
# Prediction monitoring jobs
To integrate more closely with external dat... | index |
---
title: Manage monitoring job definitions
description: Manage monitoring job definitions
---
# Manage monitoring job definitions
To view and manage monitoring job definitions, select a deployment on the **Deployments** tab and navigate to the **Job Definitions > Monitoring Jobs** tab.
 or [single record predictions](#single-record-predictions).
!!! note
... | app-make-pred |
---
title: View prediction results
description: View prediction information and insights for individual predictions in No-Code AI Apps.
---
# View prediction results {: #view-prediction-results }
The prediction results page displays prediction information and insights based on the values entered for an individual pr... | app-analyze-result |
---
title: Use applications
description: Test different No-Code AI App configurations before sharing the app with end-users.
---
# Use applications {: #use-applications}
On the **Applications** tab, click **Open** next to the application you want to launch—from here you can test different application configura... | index |
---
title: Feature Discovery support in No-Code AI Apps
description: Create No-Code AI Apps from Feature Discovery projects.
section_name: Apps
maturity: public-preview
platform: cloud-only
---
# Feature Discovery support in No-Code AI Apps {: #feature-discovery-support-in-no-code-ai-apps }
!!! info "Availability inf... | app-ft-cache |
---
title: Prefill application templates
description: Prefill applications upon creation to more easily visualize the end-user experience.
section_name: Apps
maturity: public-preview
---
# Prefill application templates {: #prefill-application-templates }
!!! info "Availability information"
Prefilled No-Code AI Ap... | app-prefill |
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